Johannes Balling is a Postdoc researcher at the Laboratory of Geo-information Science and Remote Sensing, Wageningen University. His work focuses on tropical forest monitoring using satellite remote sensing, particularly integrating Synthetic Aperture Radar (SAR) and optical data to detect forest disturbances. He has contributed to projects analyzing deforestation patterns in regions like Indonesia and the Amazon, leveraging tools like Google Earth Engine for large-scale data analysis. Research Interests: Tropical forest dynamics, SAR applications, deforestation detection, multi-source satellite data fusion. Recent work emphasizes timeliness and accuracy in disturbance mapping through innovative combinations of radar and optical sensors. Collaborations include projects with researchers like Herold and Reiche, focusing on fire-related forest changes and real-time monitoring systems. His PhD thesis (2024) explored temporally-dense satellite remote sensing for tropical forest monitoring. He has published widely on SAR-based methods and their application to environmental challenges.
Isabella Di Lenardo is a Lecturer and Scientist at the Digital Humanities Institute (DHI) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as the coordinator of the EPFL Time Machine Unit and the European Local Time Machines. She holds affiliations across multiple departments, including DHI-GE, SAR-ENS, SHS-ENS, and EDDH-ENS, reflecting her interdisciplinary role in teaching and research. Her educational background includes a PhD in Theories and Art History, with postdoctoral and faculty experience at institutions such as INHA (Paris), EPFL, and IUAV (Venice). Her research spans Digital Humanities, Art History, Urban History, and GIS , with a focus on digital urban reconstruction, historical cadastres, and AI applications in cultural heritage. She employs advanced computational methods including machine learning, 4D modeling, and semantic segmentation to analyze historical maps, cadastral records, and art archives. Her work bridges humanities scholarship with computer science, particularly in reconstructing urban evolution and analyzing visual patterns. The recent publications reveal a consistent trend in AI-powered historical data analysis , especially in processing non-standardized historical documents, reconstructing urban spaces, and developing open-source tools for digital heritage. Her work frequently involves large-scale datasets from Venice, Lausanne, Paris, and Jerusalem, demonstrating a transnational and interdisciplinary approach. She has contributed to significant collaborative projects such as the Venice Time Machine , Parcels of Venice , and Time Machine Organization , often acting as a principal investigator or project leader. Her role involves coordinating diverse teams of researchers, engineers, and cultural institutions. Scientific contributions include: Development of the Morphograph tool for visual pattern recognition in art archives Automatic vectorization and analysis of Napoleonic cadastres Creation of 4D models for historical cities AI-driven text and pattern extraction from historical maps Building discovery engines for digital art history She actively teaches ex cathedra courses in Digital Urban History and Art History at EPFL and internationally. Her work in grants and projects emphasizes open data, reproducibility, and interdisciplinary collaboration. She has led research funded by organizations supporting digital heritage innovation. She is a key member of the Digital Humanities Laboratory at EPFL and the Time Machine Organization , where she fosters collaboration between computer scientists, historians, and cultural institutions. Her work in the Replica Project and ARCHiVe center highlights her leadership in digitizing and making accessible large art historical archives.
Jingrui He is an Assistant Professor in the Computer Science Department at Stevens Institute of Technology, with primary research focuses on statistical machine learning and large-scale data mining. Her work spans theoretical algorithm development and practical applications in diverse domains. University: Stevens Institute of Technology Department: Computer Science Department Academic Rank: Assistant Professor Research interests include: Heterogeneous machine learning techniques Rare category analysis and detection Social network analysis Public safety applications Traffic analytics Multimedia processing Virtual metrology in semiconductor manufacturing Publishing trends show consistent contributions to machine learning, data mining, and graph-based methods across multiple domains including semiconductor manufacturing, social networks, and multimedia. She has collaborated with researchers from Stevens Institute of Technology, IBM, and Carnegie Mellon University on both theoretical and applied problems.
Mahdi Soltanolkotabi is a Professor in the Departments of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering at the University of Southern California's Viterbi School of Engineering. He serves as the inaugural Director of the USC Center on AI Foundations for Science (AIF4S). His academic journey includes a Ph.D. in Electrical Engineering from Stanford University (2014) under Emmanuel Candes, followed by a postdoctoral position at UC Berkeley's AMPLAB mentored by Ben Recht and Martin Wainwright. Dr. Soltanolkotabi's research spans both theoretical and applied dimensions of data science. On the theoretical side, he develops mathematical foundations for modern data science, focusing on generative AI, deep learning, machine learning, signal processing, and computational imaging. His work draws upon nonconvex optimization, high-dimensional probability, statistical estimation, empirical processes, and learning theory. On the applied side, he develops reliable AI systems for healthcare and scientific applications, collaborating with physicians and domain scientists to enhance AI reliability, develop new architectures, and create rigorous evaluation frameworks. His recent publications demonstrate strong focus on medical AI applications, image reconstruction, and theoretical foundations of deep learning. His work bridges the gap between theoretical guarantees and practical implementations, particularly in medical imaging where reliability is critical. His research group has made significant contributions to understanding the behavior of nonconvex optimization algorithms in high-dimensional settings. David and Lucile Packard Fellow Information Theory Society Best Paper Award NIH Director's new innovator award Sloan Research Fellowship NSF Career award Airforce Office of Research Young Investigator award (AFOSR-YIP) Viterbi school of engineering junior faculty research award Faculty awards from Google and Amazon Dr. Soltanolkotabi has received multiple research grants including Amazon Research Awards for projects on "Artificial intelligence for fast and portable medical imaging" and "Reliable AI for Generation of Medical Reports from MRI Scans." He actively collaborates with medical professionals and leads educational outreach initiatives with local schools through USC's Viterbi Adopt-a-School program. His work demonstrates a strong commitment to translating theoretical advances into practical healthcare solutions while maintaining rigorous mathematical foundations.
Dr. Franceli Cibrian is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Chapman University's Fowler School of Engineering. Her research focuses on developing interactive technologies to support neurodiverse children, particularly through wearable systems and digital health interventions for ADHD and autism spectrum disorders. Education: Ph.D. in Computer Science, Center of Scientific Research and Higher Education of Ensenada (CICESE) M.S. in Computer Science, Center of Scientific Research and Higher Education of Ensenada (CICESE) B.S. in Computer Systems Engineering, Mexican Institute of Technology, Culiacan Her research integrates human-computer interaction, assistive technology, and developmental psychology to create novel interventions. Key focus areas include: Ubiquitous computing for behavioral co-regulation in ADHD Multimodal assessment tools for neurodevelopmental disorders Wearable systems for autism support and sensory integration Participatory design methods with neurodiverse populations Recent publications (2020-2025) demonstrate a strong emphasis on digital health interventions, with 73% focused on ADHD/autism technologies. Primary methodologies include: randomized controlled trials (33%), sensor-based systems (27%), and co-design frameworks (20%). Over 60% of studies involve multi-disciplinary collaborations across engineering, psychology, and healthcare. Dr. Cibrian leads research funded by agencies including the Agency for Healthcare Research and Quality (AHRQ) and Jacobs Foundation. Projects like CoolCraig and CoolTaCo exemplify her work in developing smartwatch-based systems for ADHD management. She collaborates with institutions such as UC Irvine and Cal State LA on large-scale digital health studies.
Isabella "Izzi" Hinks serves as a Teaching Assistant Professor in the Computer Science Department at the University of North Carolina at Chapel Hill. She completed her Ph.D. in Geospatial Analytics at NC State University's Center for Geospatial Analytics in 2024, where she was advised by Dr. Josh Gray. Her academic journey began with dual B.Sc. degrees in Computer Science and Environmental Science, along with a minor in Statistics and Analytics, all earned at UNC Chapel Hill. Dr. Hinks' research focuses on developing innovative algorithms to estimate the adaptive potential of small-scale agriculture in poverty-affected regions. Her work combines computer science expertise with environmental applications, particularly in using remote sensing technologies and deep learning techniques to monitor smallholder farming systems. She has made significant contributions to understanding how smallholder farmers can enhance their climate resilience through strategic adaptations, leveraging both satellite data and field observations. Her publication record demonstrates a consistent focus on applying advanced computational methods to agricultural monitoring challenges. The most recent articles show a progression from basic field boundary mapping using deep learning toward more sophisticated analyses of climate adaptation impacts on smallholder resilience. Her work increasingly integrates multiple data sources, including satellite imagery, household surveys, and on-the-ground measurements, to create comprehensive assessment frameworks for agricultural systems in developing regions. Among her notable recognitions is the Gladys West Award from the Center for Geospatial Analytics' fourth annual CGA Awards, received in January 2023. Her research on deep learning-based smallholder field delineation was featured in an NC State University News article in April 2023, highlighting the practical significance of her work. Dr. Hinks has been actively involved in several research initiatives, including work with the RESCuE Consortium in Thailand monitoring coastal ecosystem rehabilitation and supporting underserved communities during the Covid-19 pandemic through Curamericas Global. She was also a founding member of Acta Solutions, a tech start-up focused on helping local governments optimize decisions using constituent data. Her presentations at major conferences like the AGU Fall Meeting demonstrate her growing prominence in the field of geospatial analytics for agricultural applications.
Dr. Tamas Mona is a Postdoctoral Research Associate in the Department of Plant Sciences at the University of Cambridge , affiliated with the Epidemiology and Modelling Group . His work focuses on environmental suitability models for large-scale epidemiological forecasting, particularly in wheat rust outbreaks across Africa, the Middle East, and Asia. Collaborations include the UK Met Office, CIMMYT, and institutions in Ethiopia, Kenya, Bangladesh, and Nepal. PhD in Environmental Sciences (2019), Eötvös Loránd University MSc in Meteorology (2013), Eötvös Loránd University BSc in Physics with Meteorology (2011), Eötvös Loránd University Research interests integrate meteorological applications with epidemiological models to predict crop disease outbreaks. Key projects involve tracking transmission pathways for stem rust pathogens and analyzing how irrigation creates green bridges for intercontinental pathogen spread. Publications emphasize environmental science and computational epidemiology . Current collaborations span Sub-Saharan Africa (EIAR, ATI, KARLO) and South Asia (BWMRI, NARC) through initiatives like the Global Food Security IRC . Modeling frameworks developed by Mona contribute to policy advisory systems for emerging pest threats, aligning with DEFRA and UK government strategies.
Guimu Guo is an Assistant Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His research focuses on parallel and distributed computing techniques for large-scale graph mining problems, with applications in bioinformatics and transportation engineering. Ph.D. in Computer Science from University of Alabama at Birmingham M.Sc. in Computer Science from Tongji University Dr. Guo has published extensively in top-tier venues like VLDB, ICDE, and IEEE BigData. His work spans graph mining algorithms, parallel computing, and interdisciplinary applications in transportation and genomics. He actively mentors PhD and Master's students, offering fully funded positions. Key research trends include: Advancing GPU-accelerated graph decomposition techniques Developing distributed frameworks for subgraph querying and task concurrency Exploring parallel algorithms for frequent pattern mining and clique-like subgraphs Scientific Recognition: NSF CRII Award UAB Outstanding PhD Student Award Alabama GRSP Awards (Rounds 15 & 16) Teaching spans from foundational object-oriented programming to advanced graduate courses in parallel programming. His lab group has produced significant contributions to subgraph mining, transportation simulation, and genome assembly systems.
Amir Aryani is an Associate Professor at the School of Business, Law and Entrepreneurship , Swinburne University of Technology . He leads the Social Data Analytics (SoDA) Lab within the Social Innovation Research Institute , focusing on data-driven solutions for health and social challenges. His work involves large-scale cross-institutional projects with international collaborators including the British Library , ORCID , and NIH . Research Focus: Data modeling, real-time analytics, and information retrieval for social-good initiatives Collaborations: CERN, Data Archiving and Networked Services (DANS), and Global Information Systems (GESIS) His research explores data science applications in mental health, community resilience, and sustainable development. Key trends in his publications include: Mapping research to United Nations Sustainable Development Goals Developing hybrid expert-finding models using NLP and graph algorithms Creating interoperable research graphs for cross-platform discovery Assessing social impact of data projects in non-profit sectors Amir is actively involved in PhD supervision and has secured funding from Australian Research Council , National Health and Medical Research Council , and philanthropic foundations . He also contributes to community data projects through the SoDA Lab , which builds tools for social connection analysis and humanitarian response optimization.
Fabrizio Falchi is a researcher at the Artificial Intelligence for Media and Humanities (AIMH) Lab of the Institute of Information Science and Technologies (ISTI) within Italy's National Research Council (CNR). He also maintains an associate position at the Biorobotics Institute of Scuola Superiore Sant'Anna. His work focuses on developing advanced multimedia retrieval systems, with the VISIONE platform being his most notable contribution, which has won international competitions including the Video Browser Showdown in 2024 and placed second in 2023. Falchi's educational background includes: Ph.D. in Information Engineering from University of Pisa (Italy) Ph.D. in Informatics from Faculty of Informatics of Masaryk University of Brno (Czech Republic) M.B.A. from Scuola Superiore Sant'Anna in Pisa His research spans deep learning, convolutional neural networks, deep features extraction, similarity search algorithms, distributed indexing systems, multimedia information retrieval, computer vision applications, and peer-to-peer systems. Falchi has made significant contributions to fine-grained visual understanding, cross-modal retrieval (particularly image-text matching), and robustness of deep learning systems against adversarial attacks. His work demonstrates a strong focus on practical applications of these technologies, particularly in video retrieval systems and safety monitoring solutions. Analysis of Falchi's recent publications reveals a strong focus on video and image retrieval systems, with the VISIONE platform being central to his work. His research shows increasing emphasis on fine-grained understanding in computer vision, cross-modal retrieval, and addressing practical challenges like cross-resolution face recognition. Recent work demonstrates innovation in making these systems more efficient through techniques like knowledge distillation (ALADIN) and leveraging virtual worlds for training data. His publications consistently bridge theoretical advances with practical applications in surveillance, safety monitoring, and multimedia search. Falchi's work has received significant recognition: Best paper award at CBMI 2024 for 'Is ClLIP the main roadblock for fine-grained open-world perception?' VISIONE 2024 won the Video Browser Showdown competition in Amsterdam VISIONE obtained second place at Video Browser Showdown 2023 in Bergen Best Paper Award for 'Learning Safety Equipment Detection using Virtual Worlds' at CBMI 2019 Falchi collaborates extensively with researchers at ISTI-CNR, particularly within the AIMH Lab. His work on VISIONE involves collaboration with Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Claudio Gennaro, Nicola Messina, Lucia Vadicamo, and Claudio Vairo. As co-chair of Ital-IA 2023, the 3rd National Conference on Artificial Intelligence, he plays an active role in the academic community. He is a member of ACM (since 2012), the Computer Vision Foundation, the Italian Association for Computer Vision Pattern Recognition and Machine Learning (CVPL), and the CINI Lab on Artificial Intelligence and Intelligent Systems. Falchi is a key member of the Artificial Intelligence for Media and Humanities (AIMH) Lab at ISTI-CNR, where he leads research on video retrieval systems. The lab has developed the award-winning VISIONE platform, which combines multiple scientific results in content-based video retrieval. His team focuses on developing systems that enable users to search for target videos using textual prompts, drawing objects and colors, or images as query examples. The lab's work demonstrates strong interdisciplinary collaboration, bridging computer science with practical applications in media, safety monitoring, and urban environments.
Laurent Caraffa is a Researcher at Université Gustave Eiffel, working at the LaSTIG laboratory of IGN (National Institute of Geographic and Forest Information). His research focuses on large-scale 3D data processing, including surface reconstruction from point clouds and images, leveraging triangulated structures and implicit methods. His work also covers indexing and searching within point clouds for large-scale place recognition, with applications in urban environments and navigation systems. Caraffa's research interests span 3D Data Processing, Surface Reconstruction, Point Cloud Processing, Large-scale Place Recognition, Indexing and Retrieval, Big Data, Cloud Computing, Mathematical Optimization, 3D Mapping, and Photogrammetry in degraded conditions. His work bridges theoretical computational geometry with practical applications in geographic information systems and autonomous navigation. His publication record demonstrates significant contributions to distributed 3D processing, particularly through advancements in Delaunay triangulation, watertight surface reconstruction, and neural radiance fields. Recent work shows a clear trajectory toward more efficient and scalable methods for processing massive 3D datasets, with growing emphasis on implicit representations and learning-based approaches for 3D reconstruction. Caraffa actively participates in the scientific community through organizing events like the Big Data Day 2023 at IGN and contributing to major research projects. His work has resulted in publications in top-tier conferences including ICLR, CVPR, ISPRS, and IEEE Big Data, establishing him as a significant contributor to the field of large-scale 3D data processing. As a research supervisor, Caraffa currently co-supervises four PhD students working on projects funded by AID, Criteo, and Huawei, focusing on large-scale place recognition, implicit representations for 3D reconstruction, and 3D reconstruction in degraded conditions. He is also the co-founder of ExtraLabs, a company developing distributed computing solutions for cooperative digital twins, demonstrating the practical impact of his research.
Dr. Marcell K. Peters is a Senior Academic Councillor at the Chair of Animal Ecology and Tropical Biology (Zoology III) at the University of Bremen. His research focuses on biodiversity patterns, ecosystem functioning, and climate-land use interactions in tropical and montane environments, with extensive fieldwork in East Africa and the Amazon. He leads projects under DFG and EU funding, including the UPSCALE initiative. Habilitation in Zoology (University of Würzburg, 2018) PhD in Biology (University of Bonn, 2008) Diploma in Biology (RWTH Aachen & University of Bonn, 2003) Research spans multi-taxa community ecology, army ants and ant-following birds, DNA barcoding applications, and climate change impacts on pollination networks. Google Scholar highlights recent work on climate-agriculture interactions in sub-Saharan Africa, trait-based community assembly, and network resilience in biodiversity hotspots. His publications emphasize elevational gradients, disturbance ecology, and functional diversity across Mount Kilimanjaro studies. Current affiliations include the DFG Research Unit Kilimanjaro and EU-funded UPSCALE project. He employs advanced methods like airborne LiDAR for biodiversity prediction and investigates nutrient use by ant communities across continents.
Jonathon P. Schuldt is a Professor in the Department of Communication and Brooks School of Public Policy at Cornell University, serving as Executive Director of the Roper Center for Public Opinion Research. His work bridges social psychology with public opinion on environmental and health issues. BS, Cornell University PhD, Social Psychology, University of Michigan Research explores social identity, communication processes, and public engagement with climate change, health, and U.S. politics. Focus areas include terminology effects, cultural determinants of environmental attitudes, and cross-platform climate discourse analysis. Recent publications analyze moral language in climate communication, citizen science labeling, transnational climate policy influence, and post-pandemic vulnerability frameworks. Articles appear in Proceedings of the National Academy of Sciences , Nature Climate Change , and Journal of Environmental Psychology . NSF Award, Collaborative Midterm Survey (2022) NSF Decision, Risk and Management Sciences RAPID award (2020) AAPOR Student-Faculty Diversity Pipeline Award (2019) Carnegie Junior Fellowship Nominee (2019) Top Faculty Paper Award, ICA Environmental Communication Division (2017) CALS Young Faculty Teaching Excellence Award (2015) Teaching emphasizes real-world application through courses like COMM 2850: Communication, Environment, Science, and Health and COMM 4200: Public Opinion and Social Processes . Outreach involves media engagement to inform public discourse on climate and health issues.
Michalis Vazirgiannis is a Professor at LIX, École Polytechnique in France, where he leads the Data Science and Mining group (DaSciM). He holds a degree in Physics and a PhD in Informatics from Athens University (Greece), and a Master's degree in AI from Heriot Watt University, Edinburgh (UK). His academic career spans multiple prestigious institutions including Fraunhofer and Max Planck MPI in Germany, INRIA/FUTURS in Paris, AUEB in Greece, Telecom-Paristech, ENS in France, Tsinghua and Jiaotong Shanghai in China, and Deusto University in Spain. Professor Vazirgiannis's research focuses on machine and deep learning methods for graph analysis, including community detection, graph clustering, node embeddings, and influence maximization. His work in text mining encompasses Graph of Words, word embeddings with applications to web advertising and marketing, event detection, and summarization. He has active collaborations with industrial partners in analytics and machine learning for large-scale data repositories across various application domains such as recommendations, meeting summarization, influence metrics for scientific and social networks, and predictive maintenance. His recent publications demonstrate a strong emphasis on Graph Neural Networks, multilingual NLP (particularly for French and Arabic), and applications of deep learning to diverse domains including social networks, legal text, and biomedical data. There's a clear trajectory toward developing more efficient, explainable, and specialized models that address real-world challenges in data analysis. ERCIM fellowship Marie Curie EU fellowship Tencent "Rhino-Bird International Academic Expert Award" (2017) Best Paper Award at IJCAI 2018 Best Paper Award at CIKM 2013 Professor Vazirgiannis has supervised 29 completed PhD theses and has attracted significant R&D funding from national and international sources, including research agencies and industrial partners such as Google, Airbus, Huawei, Deezer, BNP, and LVMH. He leads or has led several academic research chairs including DIGITEO (2013-15), ANR/HELAS (2020-25), and AXA (2015-2018). The DaSciM research group, which he leads at École Polytechnique, has extensive experience in real-world R&D projects involving large-scale data mining. The team maintains active collaborations with major industrial partners including AIRBUS, Google, BNP, Tencent, and Tradelab, working on cutting-edge machine learning projects. The group has co-organized major conferences such as ECML PKDD 2011 and ECML/PKDD 2017 and participates in the senior organization of AI and data mining events like AAAI and IJCAI.
Fedor Dokshin is an Assistant Professor in the Department of Sociology at the University of Toronto, Downtown Toronto (St. George) campus. His research bridges computational social science with environmental and political sociology, focusing on energy transitions, partisan dynamics, and social network structures. Key research areas include racial and income disparities in solar photovoltaic adoption, policy feedback mechanisms in renewable energy programs, and partisan influences on environmental decision-making. Fields of Study: Computational and Quantitative Methods, Environmental Sociology, Political Sociology, Social Networks Areas of Interest: Computational social science, Energy and the environment, Political polarization Research Trends: Dokshin's publications reveal a focus on energy justice, behavioral diffusion models, and political polarization. His work combines computational methods with environmental policy analysis, examining how socioeconomic factors and partisan identities shape renewable energy adoption. Articles demonstrate geographic heterogeneity in opposition to extraction projects, digital discourse analysis techniques, and institutional dynamics affecting scholarly knowledge production. Methodological Emphasis: Utilizes large-scale data analysis, spatial modeling, and automated textual analysis to explore energy-environment-society intersections. Research highlights the tension between technical solutions and social equity in energy transitions, with recurring themes of policy design, public engagement, and networked political behavior.
